Currently there is a growing demand for low power small-size wireless sensors used in many applications such as environmental observation, biomedical signal monitoring, and security surveillance systems. Since such applications require a wireless sensor operating in “always-on” mode, the increased data rate and limited power constraint of the sensor are significant design challenges. For example, the battery of a surveillance wireless image sensor needs to be replaced every two days. This power-and-speed trade-off limits such sensors to be deployed in the field.
Asynchronous sensing becomes attractive to solve some of the above problems. In most of the “always-on” monitoring applications, the input signal is sparse or pulse-based, while only the rapid variation of the signal is of interest. A conventional architecture with a constant sampling rate generates a large amount of null data and wastes considerable energy when sensing a sparse signal. In contrast, asynchronous sensing generates a trigger pulse only when the input amplitude crosses a set of predefined thresholds and therefore converts the analog input signal into asynchronous digital pulse sequences. Many conventional asynchronous sensing systems have been investigated in the past but have been met with very limited success and have many drawbacks.
In 1966, asynchronous sensing was first proposed by H. Inose, T. Aoki, and K. Watanabe that coined the name “asynchronous delta modulation.” [“Asynchronous delta-modulation system,” in Electronics Letters, vol. 2, no. 3, pp. 95-96, 1966]. Other investigators based on this approach of asynchronous delta modulation, have proposed variable resolution analog-digital converters (ADCs). [S. O'Driscoll and T. Meng, “Adaptive resolution ADC array for neural implant,” in 2009 Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 1053-1056, 2009], and integration pulse-based asynchronous ADCs [J. Harris, J. Principe, J. Sanchez, D. Chen, and C. She, “Pulse-based signal compression for implanted neural recording systems,” in Proceedings of 2008 IEEE International Symposium on Circuits and Systems, pp. 344-347, 2008], and level crossing sampling ADCs [M. Trakimas and S. Sonkusale, “An adaptive resolution asynchronous ADC architecture for data compression in energy constrained sensing applications,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 5, pp. 921-934, 2011].
Attempts to understand the fundamental principle of asynchronous sampling have also been previously reported. [L. Fesquet, G. Sicard, and B. Bidandgaray-Fesquet, “Targeting ultra-low power consumption with non-uniform sampling and filtering,” in Proceedings of 2010 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 3585-3588, 2010]. Later, applications related to ultrasound and audio/ECG recording have been investigated by K. Kozmin, J. Johansson, and J. Delsing [“Level-crossing ADC performance evaluation toward ultrasound application,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 56, no. 8, pp. 1708-1719, 2009]; and T. Wang, D. Wang, P. Hurst, B. Levy, and S. Lewis. [“A level-crossing analog-to-digital converter with triangular dither,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 56, no. 9, pp. 2089-2099, 2009.].
Investigators B. Schell and Y. Tsividis found that the power saving advantage of asynchronous sensing generally lies in the fact that if an input signal is inactive or no changes are detected, then no sampling and transmission is made and energy is saved. [“A continuous-time ADC/DSP/DAC system with no clock and with activity-dependent power dissipation,” in IEEE Journal of Solid-State Circuits, vol. 43, no. 11, pp. 2472-2481, 2008].
One drawback to all the above previous asynchronous sensing methods is that current methodologies do not implement wireless communication radio devices. In order to implement wireless sensing, sensors and radios must be seamlessly combined. Nonetheless, current prevalent synchronous wireless communication devices cannot match the asynchronous sensing front-end because synchronous digital radio interfaces require clocked data packets and thus must be redesigned to enable an asynchronous operation. Furthermore, asynchronous radios require wideband carriers that are not well suited for narrow-band radio applications.
Ultra-Wideband impulse radio (UWB-IR) is a competitive candidate for asynchronous radio. However, previous UWB impulse radios require external synchronizers or internal delay locked loops (DLL) that require more energy than without such elements. [see, R. Dokania, X. Wang, S. Tallur, and A. Apsel, “A low power impulse radio design for body-area networks,” in IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 58, no. 7, pp. 1458-1469, 2011; and Y. Zheng, Y. Tong, C. W. Ang, Y.-P. Xu, W. G. Yeoh, F. Lin, and R. Singh, “A CMOS carrier-less UWB transceiver for WPAN applications,” in Digest of Technical Papers, IEEE International Solid-State Circuits Conference, pp. 378-387, 2006].
Furthermore, use of a self-synchronized OOK (on-off-keying) and also applied DLLs have the drawback of constantly consuming energy even when there is no data transmission. [M. Crepaldi, C. Li, J. Fernandes, and P. Kinget, “An ultra-wideband impulse-radio transceiver chipset using synchronized-OOK modulation,” in IEEE Journal of Solid-State Circuits, vol. 46, no. 10, pp. 2284-2299, 2011].
Therefore there still exists a critical need for a low-complexity low-power wireless sensor architecture capable of providing reliable high data-rate transmission over a noisy communication link.